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AI ticket triage that checks before it routes

Every ticket read, checked and sent to the right queue

For a US telecom we built an agent that reads error tickets and handwritten notes, and error processing became 10x more efficient.

Book a free consultation How we build agents
service-desk

  1. DispatcherYour team

    The overnight tickets are in. Can the triage agent sort them before the morning shift starts?

  2. Triage agentAgent

    1. helpdesk.read_ticket text and a photo of notes
    2. ocr.read_handwriting the technician's notes
    3. network.get_status device and line status
    4. accounts.lookup customer and service plan

    Classified as a line fault. The handwritten account number is unclear, so routing waits for a check.

    Needs a personRouting with an unclear fieldLine fault, handwritten account number
    Approved by Dispatcher

  3. Triage agentAgent

    1. helpdesk.assign network queue, high priority
    2. helpdesk.add_note summary and source fields

    Sent to the network queue with a summary, the line status and the scanned notes attached.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

Tickets wait in one queue until someone reads them.

Agents read, check and route each ticket on arrival, and people take the ones that need judgement.

  1. 01

    Where it stallsEvery ticket starts with minutes of sorting.

    What we do An agent reads each ticket, sets category and priority, and fills the fields the form left empty.

  2. 02

    Where it stallsTickets land with the wrong team and bounce back.

    What we do Routing rules live in code, and the agent checks the account and system status before it assigns.

  3. 03

    Where it stallsUrgent issues hide among routine requests.

    What we do Outages and repeat failures are flagged on arrival and go straight to the person on call.

What we build

What we build for service desks

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Queues we route

  • IT service desk
  • Customer support
  • Facilities
  • HR requests
  • Access requests
  • Incident reports
  • Bug reports
  • Billing disputes

How we build it

  • Ticket reading and classification

    Text, attachments and handwritten notes turned into category, priority and the missing fields.

  • Routing and escalation rules in code

    Your queues, owners and escalation rules written as tested code the agent follows.

  • Helpdesk and system connections

    Access to your helpdesk, monitoring and account systems, with the permissions you set.

  • Accuracy checks on closed tickets

    Classification tested on your closed tickets before launch, and every routing decision logged.

Not seeing your case? Bring the queue with the longest backlog and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

AI ticket triage, answered

What is AI ticket triage? +
AI ticket triage uses an agent to read each new ticket, decide what it is about, set its priority, fill fields the requester left out and send it to the right queue. A triage agent also checks related systems, such as account or device status, before it assigns. Tickets it cannot classify with confidence go to a person.
Which tickets should AI triage first? +
Start with a queue that is high in volume and repetitive, where your team spends the first minutes of each ticket working out what it is. Error reports, access requests and field service tickets are common starting points. Agree a routing accuracy threshold with your service desk lead and test the agent on a set of tickets your team has already closed.
Have you built AI ticket routing before? +
Yes. For a US telecom we built a monitoring agent for error tickets that arrive with handwritten technician notes. It decodes the notes, analyses the error and steers the ticket, and error processing became 10x more efficient. For a call center we built a voice assistant that verifies the caller, classifies the request and routes it to the right department or escalates it.
How do you stop an AI agent from routing tickets to the wrong team? +
Routing rules are written as code, and the model only reads and classifies. When its confidence is low or a key field is unclear, the ticket waits for a person. Each decision is logged with the fields and status checks behind it, so your team can correct a route and the correction goes into the test set.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
From the blog

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